Sure 👍 Here are simple Python examples of map(), filter(), zip(), and regression—useful for interviews and practice.

 

1. map()

Used when you want to apply a function to every element.

numbers = [1, 2, 3, 4, 5]

squares = list(map(lambda x: x * x, numbers))

print(squares)

Output:

[1, 4, 9, 16, 25]

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2. filter()

Used when you want to select elements that satisfy a condition.

numbers = [1, 2, 3, 4, 5, 6]

even_numbers = list(filter(lambda x: x % 2 == 0, numbers))

print(even_numbers)

Output:

[2, 4, 6]

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3. zip()

Used to combine two or more iterables element by element.

names = ["Rahul", "Amit", "Priya"]
marks = [85, 90, 95]

students = list(zip(names, marks))

print(students)

Output:

[('Rahul', 85), ('Amit', 90), ('Priya', 95)]

You can also create a dictionary:

names = ["Rahul", "Amit", "Priya"]
marks = [85, 90, 95]

student_marks = dict(zip(names, marks))

print(student_marks)

Output:

{'Rahul': 85, 'Amit': 90, 'Priya': 95}

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4. Regression Example in Python

A simple Linear Regression example:

from sklearn.linear_model import LinearRegression

# X = hours studied
X = [[1], [2], [3], [4], [5]]

# y = marks
y = [20, 40, 50, 70, 80]

model = LinearRegression()

# Train the model
model.fit(X, y)

# Predict marks for 6 hours of study
prediction = model.predict([[6]])

print(prediction)

The model learns a relationship like:

Hours Studied → Marks

So after training, if you give:

model.predict([[6]])

it predicts the expected marks for 6 hours of study.

Quick interview difference

Function	Purpose
map()	Transform every element
filter()	Select elements based on condition
zip()	Combine multiple iterables
Regression	Predict a continuous numerical value

Easy way to remember:

map → change
filter → select
zip → combine
regression → predict